Bibliographic record
Abstract
What drew Rabindranath to Kabir? Although a poet’s purpose is never entirely transparent, the enquiry here is necessary to understand the ‘myriad-minded’ reach of Rabindranath’s art. In India’s cultural history as well, it is worth asking the question because One Hundred Poems of Kabir represents the merger of two of India’s foremost cultural institutions, Kabir and Rabindranath, within the global institution of English. From his early teens, Rabindranath had displayed the spiritual power of his poetic imagination, which progressed from awe at the distant father figure of the upaniṣads to an intense relationship with the personal godhead of vaiṣṇava poetry, especially as in Jayadeva’s Gitagovinda. But it was when he came upon Kabir’s poems that he found an answering echo to his growing attraction for a philosophically more sophisticated idea of an abstract, nirguṇa deity who could nonetheless be a viably and personally realised presence. His English versions of Kabir’s poems are necessarily, then, approximations of that resonance, which explains their closeness in form to Rabindranath‘s own expression of spirituality, most notably in his Gitanjali. My aim here is to understand the Kabir–Rabindranath unity by viewing Rabindranath’s translations in the light of his own poetry of spiritual self-affirmation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".